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Record W4296848534 · doi:10.3390/land11101628

Ecological Civilization in Practice: An Exploratory Study of Urban Agriculture in Four Chinese Cities

2022· article· en· W4296848534 on OpenAlexaff
Alesandros Glaros, Geoff Luehr, Zhenzhong Si, Steffanie Scott

Bibliographic record

VenueLand · 2022
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicUrban Agriculture and Sustainability
Canadian institutionsBalsillie School of International AffairsUniversity of WaterlooUniversity of Guelph
Fundersnot available
KeywordsUrban agricultureContext (archaeology)GrassrootsChinaAgricultureUrban planningGeographyFood securityUrban ecosystemSloganEconomic growthPolitical scienceEnvironmental planningEcologyUrbanizationEconomics

Abstract

fetched live from OpenAlex

Chinese development priorities have, since 2012, been formally framed under the slogan “Ecological Civilization” (EC). Simultaneously, urban agriculture (UA) has emerged as a potential strategy to contribute to urban food security in China, in wake of the COVID-19 pandemic. In this paper, we interrogate EC as an approach to urban and agricultural development in China and explore how EC manifests in practical terms, through a case study of urban agriculture. Over four months, we conducted on-site interviews and surveys with UA practitioners in four Chinese cities to understand how their experiences are negotiated with the state, in the context of EC. We find through our case study that capital-intensive and peri-urban approaches to UA are favoured in the context of EC, while small-scale intra-urban initiatives are actively discouraged in policy but passively accepted in practice and enforcement. This is despite all forms of UA promoting key goals for EC, including beautifying urban areas, increasing the quality of life for urban residents, and reconnecting individuals with food growing culture. Despite novel developments in innovative agricultural practices in both rural and urban contexts, the EC pathway risks overlooking grassroots initiatives and meeting local residents’ needs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.115
Threshold uncertainty score0.900

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.016
GPT teacher head0.235
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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